Pretrade Liquidity Cost Modeling
objective
Estimate pre-trade cost and liquidity risk with robust forecast diagnostics.
workflow
- •define pre-trade horizon and benchmark assumptions.
- •assemble liquidity features from depth, spread, and turnover.
- •estimate expected impact, timing risk, and completion probability.
- •validate forecasts against realized post-trade outcomes.
- •release only when pre-trade forecasts are calibrated and stable.
required diagnostics
- •pre-trade impact forecast error by size bucket.
- •completion probability calibration by urgency tier.
- •liquidity regime classification stability.
- •expected-cost versus realized-cost tracking error.
- •order difficulty score drift over time.
risk controls
- •enforce confidence bounds on forecasted costs.
- •enforce manual review for low-confidence predictions.
- •enforce periodic model recalibration and challenger testing.
outputs
- •run
python scripts/pretrade_liquidity_cost_modeling_diagnostics.py input.csv --output diagnostics.jsonand keep the json artifact. - •write an implementation memo using
references/pretrade-liquidity-cost-modeling-playbook.mdwith assumptions, tests, limits, and rollout plan.
resources
- •use
scripts/pretrade_liquidity_cost_modeling_diagnostics.pyfor deterministic diagnostics. - •use
references/pretrade-liquidity-cost-modeling-playbook.mdfor the domain checklist and delivery structure.